The present invention relates generally to information search and retrieval systems, and more particularly, navigation using electronic search engines. More particularly, the present invention relates to a user guided search navigation of search engine results.
Currently, Internet search or web search has been driving all internet related economics. There are many types of search engines doing Internet/web search with search engines now available for searching content based on traditional text, images, .pdf documents, and even audio content. Current search engines technology adapted for Internet searching are available from Google®, Yahoo!® and MSN®, for example, however there are also search engines available in recent times employing innovative technologies like Chacha® offering search using human assistance, and Guriji® offering search using native language support. There are search engine tools that collect search results from various sources and display them in a single location, like Dogpile®. A traditional search such as Google® displays the result set as a list one after another based on the page rank value for each of them.
The current manner in which all search engines operate is, they search a set of keywords that are indexed and select the top 50 or 100 based on a ranking. For example, the Page Rank® algorithm used by Google® uses the incoming and outgoing hyperlinks to a web page to compute a page rank vector for the pages. FIG. 1 illustrates an example search result listing 10, obtained via a query run through a Google® search engine, where the results (i.e., web page URLs) 12 are obtained based on a ranking application and displayed for a user via a user's (client device) browser device.
The problem with the page rank algorithm is that each webpage includes a piece of information and a searcher has to connect these information for a particular searching to understand the concept he/she is searching in the internet. For example, User 1 searches “Perl for DB Interaction”, and finds results ranked webpages 1, 2, 5 interesting. Similarly, User 2 searches for “Perl for DB interaction” and finds pages 1, 4, 5 to be relevant. User 1 and 2 also browsed in the following order 1->2->5 and 1->4->5 respectively. For this example, neither of the users browsed the hyperlinks within the page, but they browsed independent webpages in certain order to obtain the information for search string “Perl for DB Interaction”.
In the above example, as shown in FIG. 1, Google® page Rank® will not be affected even after a multitude number of searchers (e.g., 1000) have browsed 1->4->5 in that order for many times. There is no way information about preferred traversal path over a set of URL for a given search is available to users. The problem of using previous search traversal information in addition to using page rank to display the results is more relevant for today's information world and with the proliferation of social-networks where there is a growing trend in sharing information and leveraging from other users internet activities.
In general this problem is found in various contexts such as Internet bookmarks (Google Bookmark), Social Bookmark (Dogear®).
Therefore, there is a need to produce a traversal pattern for a given or related query that captures the behavior of different users in the most efficient manner to guide a subsequent searcher through a plethora of search engine results.
It would be desirable to provide, for use in conjunction with an internet search browser, the ability to navigate a user, via a browser device, towards a target search content or information, by making use of that user's and/or other users' previous search result traversal information.